1. Field of the Invention
The present invention generally relates to a computer-based method for business management and, more particularly, to a computer implemented method for generating brand-specific inventory planning and ordering information, based on consumer characterization and segmentation data calculated from point-of-sale data and other market data.
2. Description of the Related Art
Researchers in the area of marketing have developed various sophisticated models for causal forecasting of demands. A subset of these causal models are employed by marketing managers to assist their devising of business strategies. One such causal model is the model of consumer choice, based largely on household panel data, which has enabled researchers to study choice behavior, brand preferences, and purchase. Consumer choice models underlie market share models, which have also been studied extensively. Market share models are viewed as useful in evaluating the competitive effects of price and promotions on market shares of each brand and involve using aggregate data at store, regional, or market level.
Terminology used for this description is in accordance with that used by persons of ordinary skill in the relevant arts and, where appropriate, is additionally defined herein. For example, from the well-known treatise Kotler, P., Marketing Management, 7th Edition, Prentice-Hall, Englewood Cliffs N.Y., 1991, the term marketing is defined as "the process of planning and executing the conception, pricing, promotion and distribution of ideas, goods and services to create exchanges that satisfy individual and organizational objectives. Operations management can be defined as the management of the direct resources required to produce the goods and services provided by an organization.
Various studies of the dynamic interface between marketing and operations are found in: Welam. U. P., On a Simultaneous Decision Model for Marketing. Production, and Finance, Management Science, 23, 9, 1977, 1005-1009; Eliashberg, J., and R. Steinberg, Marketing-Production Decisions in an Industrial Channel of Distribution, Management Science, 33, 8, 1987, 981-1000; Porteus, E., and S. Whang, On Manufacturing/Marketing Incentives, Management Science, 37, 9, 1991, 1166-1181; Rajan, A., Rakesh, and R. Steinberg, Dynamic Pricing and Ordering Decisions by a Monopolist, Management Science, 38, 2, 1992, 240-262; and Sogomonian, A. G., and C. S. Tang, A Modeling Framework for Coordinating Promotion and Production Decisions within a Firm, Management Science, 39, 2, 1993, 191-203. However, most of the previous work has studied the dynamics of the marketing-operations interface using only one product with deterministic demands. For example, the above-cited work by Porteus and Whang has considered a single-period model with multiple end-products. The cited Porteus and Whang model focuses on developing appropriate incentives to make the efforts of "selfish" marketing and operations managers result in a global optimal. Neither that model nor the other above-cited models, however, focus on interactions between different brands and the effect of competition.
Marketing forecasts of product consumption and predictions of the success of impending marketing strategies to sell a product is extremely important to operational management which base product production schedules on marketing forecasts and predictions. However, marketing is an extremely dynamic field and, therefore, a good market model for a particular product category must consider a wide range of variables to ensure the best model possible. Heretofore none of the studies or research undertaken have sufficiently brought together or proposed an integrated system which allows for inter-firm cooperation/decision-making between marketing and operational management using a complete market model which is geared toward product production and allows for multiple competitors, marketing strategies, anticipated customer consumption, interaction with like product brands, and overall market health.
Previous researchers have identified market models combining some features of what is termed as a micro-level analysis, which is based on direct survey-type consumer choice data, with what is termed as a macro-level analysis, which is based on aggregate data-based market share information. One example is Russell, G. J., and W. A. Kamakura, Understanding Brand Competition using Micro and Macro Level Scanner Data, Journal of Marketing Research, 31, 1994, 289-303, (the Russell, et al., Understanding Brand Competition model). There are, however, shortcomings in this method. One is that it does not consider or model linkage between marketing management and the manufacturing/inventory operations of the firm. Another, as will be understood to one of ordinary skill from the description of the present invention below, is that the Russell, et al., Understand Brand Competition Model does not allow its explanatory or marketing mix variables to be selected as Multi-nominal Logit (MNL) or Multiplicative Competitive Interaction (MCI) variables. Instead, that model sets all of its explanatory or marketing mix variables as MNL variables.